Overview

Dataset statistics

Number of variables21
Number of observations185
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.5 KiB
Average record size in memory168.7 B

Variable types

NUM14
CAT6
BOOL1

Warnings

SG_UF has constant value "185" Constant
DS_CARGO has constant value "185" Constant
ANO_ELEICAO has constant value "185" Constant
NR_TURNO is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
CD_TIPO_ELEICAO is highly correlated with NR_TURNO and 8 other fieldsHigh correlation
CD_ELEICAO is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
CD_CARGO is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
SQ_CANDIDATO is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
CD_SITUACAO_CANDIDATURA is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
CD_DETALHE_SITUACAO_CAND is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
NR_PARTIDO is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
SQ_COLIGACAO is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
QT_VOTOS_NOMINAIS is highly correlated with CD_TIPO_ELEICAO and 8 other fieldsHigh correlation
RESULTADO is highly correlated with NR_CANDIDATOHigh correlation
NR_CANDIDATO is highly correlated with RESULTADOHigh correlation
NR_CANDIDATO is highly correlated with NM_URNA_CANDIDATOHigh correlation
NM_URNA_CANDIDATO is highly correlated with NR_CANDIDATOHigh correlation
df_index has unique values Unique
NM_MUNICIPIO has unique values Unique
CD_MUNICIPIO has unique values Unique

Reproduction

Analysis started2020-10-13 11:54:51.870406
Analysis finished2020-10-13 11:55:43.488465
Duration51.62 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct185
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92
Minimum0
Maximum184
Zeros1
Zeros (%)0.5%
Memory size1.4 KiB
2020-10-13T08:55:43.701330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.2
Q146
median92
Q3138
95-th percentile174.8
Maximum184
Range184
Interquartile range (IQR)92

Descriptive statistics

Standard deviation53.54904294
Coefficient of variation (CV)0.5820548146
Kurtosis-1.2
Mean92
Median Absolute Deviation (MAD)46
Skewness0
Sum17020
Variance2867.5
MonotocityNot monotonic
2020-10-13T08:55:43.957192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18410.5%
 
5710.5%
 
6610.5%
 
6510.5%
 
6410.5%
 
6310.5%
 
6210.5%
 
6110.5%
 
6010.5%
 
5910.5%
 
Other values (175)17594.6%
 
ValueCountFrequency (%) 
010.5%
 
110.5%
 
210.5%
 
310.5%
 
410.5%
 
ValueCountFrequency (%) 
18410.5%
 
18310.5%
 
18210.5%
 
18110.5%
 
18010.5%
 

SG_UF
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
PE
185 
ValueCountFrequency (%) 
PE185100.0%
 
2020-10-13T08:55:44.223028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T08:55:44.362956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:44.491886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

NM_MUNICIPIO
Categorical

UNIQUE

Distinct185
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
SANHARÓ
 
1
LAGOA DO OURO
 
1
CUSTÓDIA
 
1
TRACUNHAÉM
 
1
BELÉM DE MARIA
 
1
Other values (180)
180 
ValueCountFrequency (%) 
SANHARÓ10.5%
 
LAGOA DO OURO10.5%
 
CUSTÓDIA10.5%
 
TRACUNHAÉM10.5%
 
BELÉM DE MARIA10.5%
 
VITÓRIA DE SANTO ANTÃO10.5%
 
SERTÂNIA10.5%
 
MARAIAL10.5%
 
ITAMBÉ10.5%
 
CARPINA10.5%
 
Other values (175)17594.6%
 
2020-10-13T08:55:44.746740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique185 ?
Unique (%)100.0%
2020-10-13T08:55:44.999107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length9
Mean length10.14054054
Min length3

CD_MUNICIPIO
Real number (ℝ≥0)

UNIQUE

Distinct185
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24564.22703
Minimum23000
Maximum30015
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:45.645160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum23000
5-th percentile23096.8
Q123590
median24511
Q325437
95-th percentile26170.8
Maximum30015
Range7015
Interquartile range (IQR)1847

Descriptive statistics

Standard deviation1094.620617
Coefficient of variation (CV)0.04456157388
Kurtosis1.725821196
Mean24564.22703
Median Absolute Deviation (MAD)926
Skewness0.6954801704
Sum4544382
Variance1198194.296
MonotocityNot monotonic
2020-10-13T08:55:45.902008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2385010.5%
 
2311610.5%
 
2313210.5%
 
2415510.5%
 
2517810.5%
 
2319110.5%
 
2363910.5%
 
2389210.5%
 
2621210.5%
 
2491010.5%
 
Other values (175)17594.6%
 
ValueCountFrequency (%) 
2300010.5%
 
2301910.5%
 
2302710.5%
 
2303510.5%
 
2304310.5%
 
ValueCountFrequency (%) 
3001510.5%
 
2633610.5%
 
2631010.5%
 
2629810.5%
 
2627110.5%
 

NM_URNA_CANDIDATO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
PAULO CÂMARA
149 
ARMANDO MONTEIRO
36 
ValueCountFrequency (%) 
PAULO CÂMARA14980.5%
 
ARMANDO MONTEIRO3619.5%
 
2020-10-13T08:55:46.266748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T08:55:46.511633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:46.799461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length12
Mean length12.77837838
Min length12

DS_CARGO
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Governador
185 
ValueCountFrequency (%) 
Governador185100.0%
 
2020-10-13T08:55:47.033326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T08:55:47.249743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:47.389182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

ANO_ELEICAO
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2018
185 
ValueCountFrequency (%) 
2018185100.0%
 
2020-10-13T08:55:47.658016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T08:55:47.815917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:47.945838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

NR_CANDIDATO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
40
149 
14
36 
ValueCountFrequency (%) 
4014980.5%
 
143619.5%
 
2020-10-13T08:55:48.186690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-13T08:55:48.376572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:48.531476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

CD_TIPO_ELEICAO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.259459459
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:48.707366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum22
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.647702528
Coefficient of variation (CV)0.729246334
Kurtosis114.0776906
Mean2.259459459
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum418
Variance2.714923619
MonotocityNot monotonic
2020-10-13T08:55:48.969203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
217695.1%
 
642.2%
 
431.6%
 
2210.5%
 
810.5%
 
ValueCountFrequency (%) 
217695.1%
 
431.6%
 
642.2%
 
810.5%
 
2210.5%
 
ValueCountFrequency (%) 
2210.5%
 
810.5%
 
642.2%
 
431.6%
 
217695.1%
 

NR_TURNO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.12972973
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:49.276013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8238512638
Coefficient of variation (CV)0.729246334
Kurtosis114.0776906
Mean1.12972973
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum209
Variance0.6787309048
MonotocityNot monotonic
2020-10-13T08:55:49.585819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
117695.1%
 
342.2%
 
231.6%
 
1110.5%
 
410.5%
 
ValueCountFrequency (%) 
117695.1%
 
231.6%
 
342.2%
 
410.5%
 
1110.5%
 
ValueCountFrequency (%) 
1110.5%
 
410.5%
 
342.2%
 
231.6%
 
117695.1%
 

CD_ELEICAO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.5297297
Minimum297
Maximum3267
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:49.773704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum297
5-th percentile297
Q1297
median297
Q3297
95-th percentile297
Maximum3267
Range2970
Interquartile range (IQR)0

Descriptive statistics

Standard deviation244.6838253
Coefficient of variation (CV)0.729246334
Kurtosis114.0776906
Mean335.5297297
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum62073
Variance59870.17438
MonotocityNot monotonic
2020-10-13T08:55:50.019550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
29717695.1%
 
89142.2%
 
59431.6%
 
326710.5%
 
118810.5%
 
ValueCountFrequency (%) 
29717695.1%
 
59431.6%
 
89142.2%
 
118810.5%
 
326710.5%
 
ValueCountFrequency (%) 
326710.5%
 
118810.5%
 
89142.2%
 
59431.6%
 
29717695.1%
 

NR_ZONA
Real number (ℝ≥0)

Distinct99
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.48108108
Minimum4
Maximum377
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:50.242683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile21.4
Q143
median71
Q398
95-th percentile136.8
Maximum377
Range373
Interquartile range (IQR)55

Descriptive statistics

Standard deviation57.06690314
Coefficient of variation (CV)0.7179935446
Kurtosis9.951675727
Mean79.48108108
Median Absolute Deviation (MAD)28
Skewness2.60879881
Sum14704
Variance3256.631434
MonotocityNot monotonic
2020-10-13T08:55:50.580331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4342.2%
 
9242.2%
 
2731.6%
 
9831.6%
 
9431.6%
 
9031.6%
 
8631.6%
 
2631.6%
 
8231.6%
 
2831.6%
 
Other values (89)15382.7%
 
ValueCountFrequency (%) 
410.5%
 
1310.5%
 
1410.5%
 
1610.5%
 
1710.5%
 
ValueCountFrequency (%) 
37710.5%
 
37210.5%
 
34410.5%
 
27210.5%
 
26510.5%
 

CD_CARGO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.389189189
Minimum3
Maximum33
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:50.805193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile3
Maximum33
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.471553791
Coefficient of variation (CV)0.729246334
Kurtosis114.0776906
Mean3.389189189
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum627
Variance6.108578143
MonotocityNot monotonic
2020-10-13T08:55:51.049042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
317695.1%
 
942.2%
 
631.6%
 
3310.5%
 
1210.5%
 
ValueCountFrequency (%) 
317695.1%
 
631.6%
 
942.2%
 
1210.5%
 
3310.5%
 
ValueCountFrequency (%) 
3310.5%
 
1210.5%
 
942.2%
 
631.6%
 
317695.1%
 

SQ_CANDIDATO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.920547371e+11
Minimum1.700006045e+11
Maximum1.87000665e+12
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:51.234927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.700006045e+11
5-th percentile1.700006045e+11
Q11.700006045e+11
median1.700006045e+11
Q31.700006045e+11
95-th percentile1.700006051e+11
Maximum1.87000665e+12
Range1.700006045e+12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.400552129e+11
Coefficient of variation (CV)0.7292463335
Kurtosis114.0776906
Mean1.920547371e+11
Median Absolute Deviation (MAD)0
Skewness9.953188499
Sum3.553012637e+13
Variance1.961546266e+22
MonotocityNot monotonic
2020-10-13T08:55:51.413814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
1.700006045e+1114176.2%
 
1.700006051e+113518.9%
 
5.100018136e+1131.6%
 
3.400012091e+1131.6%
 
1.87000665e+1210.5%
 
5.100018154e+1110.5%
 
6.800024182e+1110.5%
 
ValueCountFrequency (%) 
1.700006045e+1114176.2%
 
1.700006051e+113518.9%
 
3.400012091e+1131.6%
 
5.100018136e+1131.6%
 
5.100018154e+1110.5%
 
ValueCountFrequency (%) 
1.87000665e+1210.5%
 
6.800024182e+1110.5%
 
5.100018154e+1110.5%
 
5.100018136e+1131.6%
 
3.400012091e+1131.6%
 

CD_SITUACAO_CANDIDATURA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.55675676
Minimum12
Maximum132
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:51.603698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q112
median12
Q312
95-th percentile12
Maximum132
Range120
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.886215165
Coefficient of variation (CV)0.729246334
Kurtosis114.0776906
Mean13.55675676
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum2508
Variance97.73725029
MonotocityNot monotonic
2020-10-13T08:55:51.787669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1217695.1%
 
3642.2%
 
2431.6%
 
13210.5%
 
4810.5%
 
ValueCountFrequency (%) 
1217695.1%
 
2431.6%
 
3642.2%
 
4810.5%
 
13210.5%
 
ValueCountFrequency (%) 
13210.5%
 
4810.5%
 
3642.2%
 
2431.6%
 
1217695.1%
 

CD_DETALHE_SITUACAO_CAND
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.259459459
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:51.972674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum22
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.647702528
Coefficient of variation (CV)0.729246334
Kurtosis114.0776906
Mean2.259459459
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum418
Variance2.714923619
MonotocityNot monotonic
2020-10-13T08:55:52.149708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
217695.1%
 
642.2%
 
431.6%
 
2210.5%
 
810.5%
 
ValueCountFrequency (%) 
217695.1%
 
431.6%
 
642.2%
 
810.5%
 
2210.5%
 
ValueCountFrequency (%) 
2210.5%
 
810.5%
 
642.2%
 
431.6%
 
217695.1%
 

NR_PARTIDO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.84864865
Minimum14
Maximum440
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:52.361578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14
Q140
median40
Q340
95-th percentile40
Maximum440
Range426
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.7396968
Coefficient of variation (CV)0.8717910889
Kurtosis97.16257951
Mean39.84864865
Median Absolute Deviation (MAD)0
Skewness8.772458547
Sum7372
Variance1206.846533
MonotocityNot monotonic
2020-10-13T08:55:52.528474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
4014176.2%
 
143518.9%
 
12031.6%
 
8031.6%
 
44010.5%
 
16010.5%
 
4210.5%
 
ValueCountFrequency (%) 
143518.9%
 
4014176.2%
 
4210.5%
 
8031.6%
 
12031.6%
 
ValueCountFrequency (%) 
44010.5%
 
16010.5%
 
12031.6%
 
8031.6%
 
4210.5%
 

SQ_COLIGACAO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.920541107e+11
Minimum1.700000501e+11
Maximum1.870000551e+12
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:52.704609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.700000501e+11
5-th percentile1.700000501e+11
Q11.700000501e+11
median1.700000501e+11
Q31.700000501e+11
95-th percentile1.700000501e+11
Maximum1.870000551e+12
Range1.700000501e+12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.400547561e+11
Coefficient of variation (CV)0.7292463339
Kurtosis114.0776906
Mean1.920541107e+11
Median Absolute Deviation (MAD)0
Skewness9.953188502
Sum3.553001048e+13
Variance1.961533472e+22
MonotocityNot monotonic
2020-10-13T08:55:52.925472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
1.700000501e+1114176.2%
 
1.700000501e+113518.9%
 
3.400001003e+1131.6%
 
5.100001504e+1131.6%
 
1.870000551e+1210.5%
 
5.100001504e+1110.5%
 
6.800002005e+1110.5%
 
ValueCountFrequency (%) 
1.700000501e+1114176.2%
 
1.700000501e+113518.9%
 
3.400001003e+1131.6%
 
5.100001504e+1131.6%
 
5.100001504e+1110.5%
 
ValueCountFrequency (%) 
1.870000551e+1210.5%
 
6.800002005e+1110.5%
 
5.100001504e+1110.5%
 
5.100001504e+1131.6%
 
3.400001003e+1131.6%
 

CD_SIT_TOT_TURNO
Real number (ℝ≥0)

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.745945946
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:53.162168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.576077029
Coefficient of variation (CV)0.9027066575
Kurtosis14.5766873
Mean1.745945946
Median Absolute Deviation (MAD)0
Skewness3.130045975
Sum323
Variance2.484018801
MonotocityNot monotonic
2020-10-13T08:55:53.351048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
114176.2%
 
43619.5%
 
331.6%
 
231.6%
 
1210.5%
 
1110.5%
 
ValueCountFrequency (%) 
114176.2%
 
231.6%
 
331.6%
 
43619.5%
 
1110.5%
 
ValueCountFrequency (%) 
1210.5%
 
1110.5%
 
43619.5%
 
331.6%
 
231.6%
 

QT_VOTOS_NOMINAIS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct183
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10791.03784
Minimum877
Maximum311792
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-13T08:55:53.589901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum877
5-th percentile2362.6
Q13682
median5358
Q38808
95-th percentile27888.8
Maximum311792
Range310915
Interquartile range (IQR)5126

Descriptive statistics

Standard deviation25604.77008
Coefficient of variation (CV)2.372781049
Kurtosis105.8779184
Mean10791.03784
Median Absolute Deviation (MAD)2208
Skewness9.450488465
Sum1996342
Variance655604250.9
MonotocityDecreasing
2020-10-13T08:55:53.822758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
297221.1%
 
290021.1%
 
210410.5%
 
624310.5%
 
173610.5%
 
598410.5%
 
235610.5%
 
2364510.5%
 
393210.5%
 
444310.5%
 
Other values (173)17393.5%
 
ValueCountFrequency (%) 
87710.5%
 
161410.5%
 
173610.5%
 
180610.5%
 
190910.5%
 
ValueCountFrequency (%) 
31179210.5%
 
10766910.5%
 
8127610.5%
 
6322910.5%
 
5752310.5%
 

RESULTADO
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
1
149 
0
36 
ValueCountFrequency (%) 
114980.5%
 
03619.5%
 
2020-10-13T08:55:53.999647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-10-13T08:54:53.291524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:53.549364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:53.829190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:54.057047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:54.255925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:54.464795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:54.680662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:54.897527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:55.150371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:55.384224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:55.675043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:55.922891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:56.201717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:56.437571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:56.705405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:56.958249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:57.194101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:57.442949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:57.666811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:57.897666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:58.138515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:58.363377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:58.607226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:58.836082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:59.115908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:59.364754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:59.648580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:54:59.899422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:00.138277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:00.387121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:00.629969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:00.865822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:01.088686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:01.322539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:01.553397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:01.766265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:02.003118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:02.231974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:02.478821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:02.699686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:02.926545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:03.158402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:03.379262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:03.593132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:03.787011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:03.981889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:04.147788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:04.323678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:04.512561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:04.704440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:04.901320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:05.076212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:05.292075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:05.484955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:05.672839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:05.854726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:06.043609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:06.242486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:06.447358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:06.654232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:06.833121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:07.034996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:07.235869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:07.443741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:07.668600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:07.896461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:08.126319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:09.132693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:09.349558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:09.561425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:09.786286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:10.025139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:10.331947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:10.571801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:10.902594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:11.126456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:11.355316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:11.671118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:11.895978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:12.110845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:12.336703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:12.532584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:12.750448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:12.978305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:13.222154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:13.460007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:13.673875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:13.896738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:14.092617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:14.299488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:14.505357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:14.702235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:14.910108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:15.102987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:15.332845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:15.509737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:15.706614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:15.895494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:16.095372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:16.402183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:16.858899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:17.332602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:17.541473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:17.760337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:18.049158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:18.458906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:18.791699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:19.075527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:19.479378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:19.739214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:20.078004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:20.359831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:20.607676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:20.994435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:21.352214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:21.655026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:21.972829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:22.234667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:22.550470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:22.755344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:22.959216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:23.148100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:23.366963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:23.557844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:23.757720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:23.942607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:24.171466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:24.430303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:24.682149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:24.954977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:25.185837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:25.460665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:26.173221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:26.482032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:26.819821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:27.153613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:27.439438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:27.777226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:28.027071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:28.390846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:28.730636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:28.964490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:29.205341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:29.555122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:29.776986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:29.992858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:30.307663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:30.499559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:30.725240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:30.922119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:31.146981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:31.335865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:31.540752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:31.726635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:31.923512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:32.156101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:32.395952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:32.639800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:32.850310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:33.077169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:33.315021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:33.529890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:33.763847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:34.036817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:34.380530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:34.657604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:34.935786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:35.205883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:35.487602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:35.785869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:36.074870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:36.311872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:36.517746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:36.735793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:36.966803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:37.168888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:37.386751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:37.589799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:37.817780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:38.007692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:38.222754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:38.428774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:38.718595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:38.943750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:39.181791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:39.426636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:39.633192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:39.848059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:40.092906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:40.343750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:40.596594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:40.810758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:41.050608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:41.251777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:41.482635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:41.706718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-13T08:55:54.153661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-13T08:55:54.700589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-13T08:55:55.254612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-13T08:55:55.813624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-13T08:55:56.275500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-13T08:55:42.191207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-13T08:55:43.159652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexSG_UFNM_MUNICIPIOCD_MUNICIPIONM_URNA_CANDIDATODS_CARGOANO_ELEICAONR_CANDIDATOCD_TIPO_ELEICAONR_TURNOCD_ELEICAONR_ZONACD_CARGOSQ_CANDIDATOCD_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDNR_PARTIDOSQ_COLIGACAOCD_SIT_TOT_TURNOQT_VOTOS_NOMINAISRESULTADO
0130PERECIFE25313PAULO CÂMARAGovernador20184022113267344331870006650017132224401870000551430113117921
185PEJABOATÃO DOS GUARARAPES24570PAULO CÂMARAGovernador2018408411883771268000241818848816068000020052041076691
2109PEOLINDA24910PAULO CÂMARAGovernador2018406389122795100018136413661205100001503903812761
3120PEPAULISTA25135PAULO CÂMARAGovernador2018406389127295100018136413661205100001503903632291
442PECARUARU23817ARMANDO MONTEIROGovernador2018146389125295100018153843664251000015044712575230
5123PEPETROLINA25216PAULO CÂMARAGovernador2018406389137295100018136413661205100001503903487071
628PECABO DE SANTO AGOSTINHO23574PAULO CÂMARAGovernador201840425941366340001209094244803400001002602461001
734PECAMARAGIBE26298PAULO CÂMARAGovernador201840425942656340001209094244803400001002602320561
864PEGARANHUNS24198ARMANDO MONTEIROGovernador20181421297563170000605128122141700000501494289460
9181PEVITÓRIA DE SANTO ANTÃO26271PAULO CÂMARAGovernador201840425941206340001209094244803400001002602281041

Last rows

df_indexSG_UFNM_MUNICIPIOCD_MUNICIPIONM_URNA_CANDIDATODS_CARGOANO_ELEICAONR_CANDIDATOCD_TIPO_ELEICAONR_TURNOCD_ELEICAONR_ZONACD_CARGOSQ_CANDIDATOCD_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDNR_PARTIDOSQ_COLIGACAOCD_SIT_TOT_TURNOQT_VOTOS_NOMINAISRESULTADO
175135PESALGADINHO25410PAULO CÂMARAGovernador2018402129788317000060454712240170000050130123561
17623PEBREJINHO23493PAULO CÂMARAGovernador2018402129799317000060454712240170000050130121041
17736PECAMUTANGA23710ARMANDO MONTEIROGovernador2018142129727317000060512812214170000050149420700
17832PECALUMBI23671PAULO CÂMARAGovernador20184021297108317000060454712240170000050130120111
179150PESOLIDÃO25879PAULO CÂMARAGovernador2018402129798317000060454712240170000050130119651
18033PECALÇADO23655ARMANDO MONTEIROGovernador2018142129794317000060512812214170000050149419090
18179PEITACURUBA24473PAULO CÂMARAGovernador2018402129773317000060454712240170000050130118061
18271PEIBIRAJUBA24333ARMANDO MONTEIROGovernador2018142129748317000060512812214170000050149417360
18376PEINGAZEIRA24414PAULO CÂMARAGovernador2018402129750317000060454712240170000050130116141
18458PEFERNANDO DE NORONHA30015PAULO CÂMARAGovernador20184021297431700006045471224017000005013018771